2008 IEEE Conference on Computer Vision and Pattern Recognition (2008)
Anchorage, AK, USA
June 23, 2008 to June 28, 2008
Yang Hu , MOE-Microsoft Key Lab of MCC, University of Science and Technology of China, Hefei 230027, China
Mingjing Li , Microsoft Research Asia, 49 Zhichun Road, Beijing 100190, China
Nenghai Yu , MOE-Microsoft Key Lab of MCC, University of Science and Technology of China, Hefei 230027, China
We study the problem of learning to rank images for image retrieval. For a noisy set of images indexed or tagged by the same keyword, we learn a ranking model from some training examples and then use the learned model to rank new images. Unlike previous work on image retrieval, which usually coarsely divide the images into relevant and irrelevant images and learn a binary classifier, we learn the ranking model from image pairs with preference relations. In addition to the relevance of images, we are further interested in what portion of the image is of interest to the user. Therefore, we consider images represented by sets of regions and propose multiple-instance rank learning based on the max margin framework. Three different schemes are designed to encode the multiple-instance assumption. We evaluate the performance of the multiple-instance ranking algorithms on real-word images collected from Flickr — a popular photo sharing service. The experimental results show that the proposed algorithms are capable of learning effective ranking models for image retrieval.
Mingjing Li, Yang Hu and Nenghai Yu, "Multiple-instance ranking: Learning to rank images for image retrieval," 2008 IEEE Conference on Computer Vision and Pattern Recognition(CVPR), Anchorage, AK, USA, 2008, pp. 1-8.